Question 1
(a)
Define data compression and explain its necessity in digital systems.
3 Marks
(b)
Differentiate between Lossless and Lossy Compression with suitable examples.
4 Marks
(c)
Explain Modeling and Coding with suitable example.
7 Marks
Gujarat Technological UniversitySummer 2026 Examination
GTU 3171615 Data Compression Summer 2026 Paper Solution & PDF
B.E. · IT Engineering · Semester 7 · Subject Code: 3171615
Total Marks70 MarksExternal theory exam
Passing Marks23 Marks33% minimum cutoff
Exam Duration2.5 Hours2:30 PM – 5:00 PM
Paper Structure5 QuestionsWith internal OR choices
Question 2
(a)
Explain Uniform Quantization with suitable example.
3 Marks
(b)
Define Rice Coding and explain its working principle with example.
4 Marks
(c)
Explain LZ77 Algorithm in detail.
7 Marks
OR OPTION
(c)
Explain LZ78 Algorithm in detail.
7 Marks
Question 3
(a)
Explain Minimum Variance Huffman Code
3 Marks
(b)
Write a short note on Golomb Code.
4 Marks
(c)
Explain Arithmetic Coding with suitable example. Compare it with Huffman coding.
7 Marks
OR OPTION
(a)
List and explain applications of Huffman Coding.
3 Marks
(b)
Write a short note on Tunstall Codes.
4 Marks
(c)
Explain Adaptive Huffman Coding with neat flow of update, encoding, and decoding procedures.
7 Marks
Question 4
(a)
Define Prediction by Partial Matching (PPM) and explain its basic concept.
3 Marks
(b)
Explain the Burrows–Wheeler Transform (BWT) with suitable example.
4 Marks
(c)
Explain the Old JPEG Standard for image compression in detail with suitable block diagram.
7 Marks
OR OPTION
(a)
Explain the role of the Escape Symbol in PPM compression.
3 Marks
(b)
Explain Move-to-Front (MTF) Coding used after the Burrows–Wheeler Transform with example.
4 Marks
(c)
Explain the CALIC (Context-Based, Adaptive, Lossless Image Coding) algorithm with its major stages and advantages.
7 Marks
Question 5
(a)
Define distortion criteria and explain its significance in lossy compression.
3 Marks
(b)
Define Quantization and explain its role in lossy compression.
4 Marks
(c)
Explain the Linde–Buzo–Gray (LBG) Algorithm for Vector Quantization in detail.
7 Marks
OR OPTION
(a)
Explain the concept of the Human Visual System (HVS) in lossy compression.
3 Marks
(b)
Differentiate between Forward Adaptive and Backward Adaptive Quantization.
4 Marks
(c)
Discuss Non-Uniform Quantization and its types. Explain PDF-Optimized Quantization and Companded Quantization with examples.
7 Marks
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About this Examination Paper & Attribution
Official Gujarat Technological University (GTU) examination paper and step-by-step solutions for Data Compression (Summer 2026, B.E. · IT Engineering, Sem 7). Features complete 70-mark regular & remedial examination pattern, official marking distribution across all 5 questions, and direct 1-click official PDF download.
Transcribed for student exam preparation from Gujarat Technological University official examination archives. Questions, syllabus guidelines, and curriculum marking schemes remain the intellectual property of Gujarat Technological University.